The present disclosure relates generally to damage propagation estimation and remaining useful life (RUL) estimation. The present disclosure relates in particular to situations in which a physics-based model of failure is not specifically known. This may be true at a system or subsystem level.
Estimating how the damage to an equipment will change over time, possibly as a function of conditions that may impact the rate of change, is imperative in understanding when an equipment will reach its end of useful life. Estimating the RUL of equipment is known in the art as prognostics. RUL estimates provide valuable information for operation of modern complex equipment. RUL estimates provide decision making aids that allow operators to change operational characteristics (such as load) which, in turn, may prolong a life of the equipment. RUL estimates also allow planners to account for upcoming maintenance and set in motion a logistics process that supports a smooth transition from faulted to fully functioning equipment. Predicting remaining useful life is not straightforward because, ordinarily, RUL is conditional on future usage conditions, such as load and speed, for example. Examples of equipment that may benefit from the use of RUL estimates are aircraft engines (both military and commercial), medical equipment, and power plants, for example.
A common approach to prognostics is to employ a materials level model of damage propagation contingent on future use. Such a model is often times based on detailed materials knowledge and makes use of finite element modeling. Because such models are extremely costly to develop, they must be limited to a few important parts of a system, but are rarely applied to all parts within a system.
Another approach to prognostics is a data-driven approach that takes advantage of time series data where equipment behavior has been tracked via sensor outputs during normal operation all the way to an end of equipment useful life. The end of equipment useful life can represent a totally non-functioning state of the equipment, for example, equipment failure, which in turn may result in system failure. The end of equipment useful life can also represent a state of the equipment wherein the equipment no longer provides expected results. When a reasonably sized set of these observations exists, pattern recognition algorithms can be employed to recognize these trends and predict RUL. These predictions are easier under the assumption of near-constant future operating conditions. However, such run to end of equipment useful life data are often not available because, when the observed system is complex, expensive, and, safety is important, such as aircraft engines, for example, faults will be repaired before they lead to the end of equipment useful life. This deprives the data driven approach from information that is necessary for its proper application.
Accordingly, there is a need in the art for a life estimation arrangement that overcomes these limitations.